Overview

Dataset statistics

Number of variables10
Number of observations2410845
Missing cells28163
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory183.9 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Assessor Neighborhood has a high cardinality: 90 distinct values High cardinality
Year Property Built has 25899 (1.1%) missing values Missing
Number of Bathrooms is highly skewed (γ1 = 43.42982487) Skewed
Number of Rooms is highly skewed (γ1 = 56.70711952) Skewed
Number of Stories is highly skewed (γ1 = 100.369881) Skewed
Number of Units is highly skewed (γ1 = 144.696288) Skewed
Property Area is highly skewed (γ1 = 76.57358633) Skewed
Lot Area is highly skewed (γ1 = 410.9422795) Skewed
Assessed Value is highly skewed (γ1 = 75.58785394) Skewed
Unnamed: 0 has unique values Unique
Number of Bathrooms has 98378 (4.1%) zeros Zeros
Number of Rooms has 157093 (6.5%) zeros Zeros
Number of Stories has 293160 (12.2%) zeros Zeros
Number of Units has 303575 (12.6%) zeros Zeros
Property Area has 30420 (1.3%) zeros Zeros
Lot Area has 597835 (24.8%) zeros Zeros

Reproduction

Analysis started2022-06-19 19:52:30.881500
Analysis finished2022-06-19 19:54:10.802607
Duration1 minute and 39.92 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct2410845
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1337842.145
Minimum138
Maximum2666116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:10.879679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum138
5-th percentile136204.2
Q1670223
median1338549
Q32004250
95-th percentile2536607.6
Maximum2666116
Range2665978
Interquartile range (IQR)1334027

Descriptive statistics

Standard deviation770209.8177
Coefficient of variation (CV)0.5757105356
Kurtosis-1.204163683
Mean1337842.145
Median Absolute Deviation (MAD)667008
Skewness-0.003571528488
Sum3.225330046 × 1012
Variance5.932231633 × 1011
MonotonicityStrictly increasing
2022-06-19T21:54:10.991781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1381
 
< 0.1%
17831071
 
< 0.1%
17830911
 
< 0.1%
17830921
 
< 0.1%
17830931
 
< 0.1%
17830941
 
< 0.1%
17830951
 
< 0.1%
17830961
 
< 0.1%
17830971
 
< 0.1%
17830981
 
< 0.1%
Other values (2410835)2410835
> 99.9%
ValueCountFrequency (%)
1381
< 0.1%
1671
< 0.1%
1751
< 0.1%
1761
< 0.1%
1791
< 0.1%
1801
< 0.1%
1831
< 0.1%
1841
< 0.1%
1851
< 0.1%
1861
< 0.1%
ValueCountFrequency (%)
26661161
< 0.1%
26661151
< 0.1%
26661141
< 0.1%
26661131
< 0.1%
26661121
< 0.1%
26661111
< 0.1%
26661101
< 0.1%
26661091
< 0.1%
26661081
< 0.1%
26661071
< 0.1%

Year Property Built
Real number (ℝ≥0)

MISSING

Distinct167
Distinct (%)< 0.1%
Missing25899
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1940.998833
Minimum1791
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:11.096875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1791
5-th percentile1900
Q11914
median1935
Q31961
95-th percentile2004
Maximum2020
Range229
Interquartile range (IQR)47

Descriptive statistics

Standard deviation32.47838956
Coefficient of variation (CV)0.01673282282
Kurtosis-0.6408308403
Mean1940.998833
Median Absolute Deviation (MAD)23
Skewness0.6273453659
Sum4629177402
Variance1054.845788
MonotonicityNot monotonic
2022-06-19T21:54:11.217986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1900176419
 
7.3%
192569071
 
2.9%
190762336
 
2.6%
192461456
 
2.5%
190861140
 
2.5%
192655391
 
2.3%
194154155
 
2.2%
192353946
 
2.2%
192751432
 
2.1%
194051148
 
2.1%
Other values (157)1688452
70.0%
ValueCountFrequency (%)
179113
< 0.1%
18003
 
< 0.1%
18082
 
< 0.1%
18293
 
< 0.1%
184826
< 0.1%
184913
< 0.1%
185026
< 0.1%
185113
< 0.1%
185326
< 0.1%
186013
< 0.1%
ValueCountFrequency (%)
202012
 
< 0.1%
2019242
 
< 0.1%
20181053
 
< 0.1%
20171236
 
0.1%
20165691
0.2%
20152734
0.1%
20145123
0.2%
20131342
 
0.1%
20124607
0.2%
2011931
 
< 0.1%

Number of Bathrooms
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct217
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.576149441
Minimum0
Maximum1002
Zeros98378
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:11.327085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum1002
Range1002
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.905069934
Coefficient of variation (CV)3.456736551
Kurtosis2909.166375
Mean2.576149441
Median Absolute Deviation (MAD)1
Skewness43.42982487
Sum6210697
Variance79.30027052
MonotonicityNot monotonic
2022-06-19T21:54:11.431180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980385
40.7%
2766287
31.8%
3264235
 
11.0%
4122996
 
5.1%
098378
 
4.1%
545089
 
1.9%
644967
 
1.9%
1212387
 
0.5%
712128
 
0.5%
811282
 
0.5%
Other values (207)52711
 
2.2%
ValueCountFrequency (%)
098378
 
4.1%
1980385
40.7%
2766287
31.8%
3264235
 
11.0%
4122996
 
5.1%
545089
 
1.9%
644967
 
1.9%
712128
 
0.5%
811282
 
0.5%
97304
 
0.3%
ValueCountFrequency (%)
100211
 
< 0.1%
85013
 
< 0.1%
7629
 
< 0.1%
71213
 
< 0.1%
62613
 
< 0.1%
60613
 
< 0.1%
56213
 
< 0.1%
56013
 
< 0.1%
54152
< 0.1%
5331
 
< 0.1%

Number of Rooms
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct384
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.442546493
Minimum0
Maximum3606
Zeros157093
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:11.540280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q38
95-th percentile19
Maximum3606
Range3606
Interquartile range (IQR)3

Descriptive statistics

Standard deviation22.99133152
Coefficient of variation (CV)2.723269755
Kurtosis6015.945078
Mean8.442546493
Median Absolute Deviation (MAD)2
Skewness56.70711952
Sum20353671
Variance528.6013252
MonotonicityNot monotonic
2022-06-19T21:54:11.642372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5475699
19.7%
6380518
15.8%
4258806
10.7%
7231673
9.6%
8168402
 
7.0%
0157093
 
6.5%
3106458
 
4.4%
9102507
 
4.3%
1096077
 
4.0%
1263503
 
2.6%
Other values (374)370109
15.4%
ValueCountFrequency (%)
0157093
 
6.5%
117337
 
0.7%
243634
 
1.8%
3106458
 
4.4%
4258806
10.7%
5475699
19.7%
6380518
15.8%
7231673
9.6%
8168402
 
7.0%
9102507
 
4.3%
ValueCountFrequency (%)
360613
< 0.1%
275013
< 0.1%
250013
< 0.1%
19194
 
< 0.1%
18243
 
< 0.1%
179213
< 0.1%
156613
< 0.1%
13537
< 0.1%
13372
 
< 0.1%
131211
< 0.1%

Number of Stories
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.621974868
Minimum0
Maximum999
Zeros293160
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:11.755475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum999
Range999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.208849116
Coefficient of variation (CV)4.444488789
Kurtosis11479.05178
Mean1.621974868
Median Absolute Deviation (MAD)1
Skewness100.369881
Sum3910330
Variance51.96750558
MonotonicityNot monotonic
2022-06-19T21:54:11.862593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11161425
48.2%
2677409
28.1%
0293160
 
12.2%
3206051
 
8.5%
425932
 
1.1%
69044
 
0.4%
57658
 
0.3%
94917
 
0.2%
74398
 
0.2%
163833
 
0.2%
Other values (91)17018
 
0.7%
ValueCountFrequency (%)
0293160
 
12.2%
11161425
48.2%
2677409
28.1%
3206051
 
8.5%
425932
 
1.1%
57658
 
0.3%
69044
 
0.4%
74398
 
0.2%
83079
 
0.1%
94917
 
0.2%
ValueCountFrequency (%)
99914
< 0.1%
9945
 
< 0.1%
9884
 
< 0.1%
9722
 
< 0.1%
96313
< 0.1%
9383
 
< 0.1%
9352
 
< 0.1%
9312
 
< 0.1%
9302
 
< 0.1%
9094
 
< 0.1%

Number of Units
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct277
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.970543108
Minimum0
Maximum4000
Zeros303575
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:11.969690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile5
Maximum4000
Range4000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.0257345
Coefficient of variation (CV)8.132648522
Kurtosis30505.61349
Mean1.970543108
Median Absolute Deviation (MAD)0
Skewness144.696288
Sum4750674
Variance256.8241662
MonotonicityNot monotonic
2022-06-19T21:54:12.076787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11620757
67.2%
0303575
 
12.6%
2220995
 
9.2%
385007
 
3.5%
455365
 
2.3%
632258
 
1.3%
518987
 
0.8%
1211664
 
0.5%
78393
 
0.3%
88359
 
0.3%
Other values (267)45485
 
1.9%
ValueCountFrequency (%)
0303575
 
12.6%
11620757
67.2%
2220995
 
9.2%
385007
 
3.5%
455365
 
2.3%
518987
 
0.8%
632258
 
1.3%
78393
 
0.3%
88359
 
0.3%
95990
 
0.2%
ValueCountFrequency (%)
400013
< 0.1%
314613
< 0.1%
21351
 
< 0.1%
19194
 
< 0.1%
19109
< 0.1%
14461
 
< 0.1%
101012
< 0.1%
9866
< 0.1%
9825
 
< 0.1%
8753
 
< 0.1%

Property Area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct12345
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3133.81705
Minimum0
Maximum4701100
Zeros30420
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:12.190005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile654
Q11106
median1500
Q32369
95-th percentile6212
Maximum4701100
Range4701100
Interquartile range (IQR)1263

Descriptive statistics

Standard deviation20754.64527
Coefficient of variation (CV)6.622800548
Kurtosis11532.37878
Mean3133.81705
Median Absolute Deviation (MAD)508
Skewness76.57358633
Sum7555147165
Variance430755300.4
MonotonicityNot monotonic
2022-06-19T21:54:12.294988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030420
 
1.3%
100020425
 
0.8%
125020332
 
0.8%
120019164
 
0.8%
150015814
 
0.7%
115015382
 
0.6%
112514449
 
0.6%
130014303
 
0.6%
80013839
 
0.6%
135012800
 
0.5%
Other values (12335)2233917
92.7%
ValueCountFrequency (%)
030420
1.3%
188
 
< 0.1%
238
 
< 0.1%
323
 
< 0.1%
55
 
< 0.1%
66
 
< 0.1%
713
 
< 0.1%
91
 
< 0.1%
1113
 
< 0.1%
453
 
< 0.1%
ValueCountFrequency (%)
47011004
 
< 0.1%
45011254
 
< 0.1%
301583713
< 0.1%
153431213
< 0.1%
147192913
< 0.1%
142423013
< 0.1%
14204302
 
< 0.1%
132000013
< 0.1%
130000013
< 0.1%
108670013
< 0.1%

Lot Area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct11770
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3075.241886
Minimum0
Maximum58001446
Zeros597835
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:12.399082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1818
median2495
Q33000
95-th percentile5397
Maximum58001446
Range58001446
Interquartile range (IQR)2182

Descriptive statistics

Standard deviation137024.3103
Coefficient of variation (CV)44.55724636
Kurtosis173217.997
Mean3075.241886
Median Absolute Deviation (MAD)705
Skewness410.9422795
Sum7413931525
Variance1.877566162 × 1010
MonotonicityNot monotonic
2022-06-19T21:54:12.501175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0597835
24.8%
2500145220
 
6.0%
2495142642
 
5.9%
3000139268
 
5.8%
2996136589
 
5.7%
175029701
 
1.2%
187322747
 
0.9%
187518563
 
0.8%
284815099
 
0.6%
174614788
 
0.6%
Other values (11760)1148393
47.6%
ValueCountFrequency (%)
0597835
24.8%
0.1213
 
< 0.1%
0.1813
 
< 0.1%
0.1913
 
< 0.1%
0.2513
 
< 0.1%
0.330
 
< 0.1%
0.3811
 
< 0.1%
1.2213
 
< 0.1%
1.6613
 
< 0.1%
1.6913
 
< 0.1%
ValueCountFrequency (%)
5800144613
< 0.1%
1007500011
< 0.1%
477415812
< 0.1%
1137903.46
 
< 0.1%
9468652
 
< 0.1%
8526868
< 0.1%
8276002
 
< 0.1%
798187.513
< 0.1%
7950333
 
< 0.1%
76562516
< 0.1%

Assessor Neighborhood
Categorical

HIGH CARDINALITY

Distinct90
Distinct (%)< 0.1%
Missing2264
Missing (%)0.1%
Memory size18.4 MiB
Inner Mission
 
87088
Central Sunset
 
76609
Noe Valley
 
73601
Parkside
 
72573
Excelsior
 
71578
Other values (85)
2027132 

Length

Max length29
Median length22
Mean length13.36312999
Min length6

Characters and Unicode

Total characters32186181
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHunters Point
2nd rowLower Pacific Heights
3rd rowHunters Point
4th rowExcelsior
5th rowRussian Hill

Common Values

ValueCountFrequency (%)
Inner Mission87088
 
3.6%
Central Sunset76609
 
3.2%
Noe Valley73601
 
3.1%
Parkside72573
 
3.0%
Excelsior71578
 
3.0%
Central Richmond66570
 
2.8%
Pacific Heights63864
 
2.6%
Outer Parkside60539
 
2.5%
Potrero Hill58478
 
2.4%
Outer Sunset56572
 
2.3%
Other values (80)1721109
71.4%

Length

2022-06-19T21:54:12.609274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
heights347021
 
7.3%
mission215319
 
4.5%
outer205742
 
4.3%
inner202125
 
4.2%
sunset185349
 
3.9%
hill179798
 
3.8%
richmond167135
 
3.5%
parkside151673
 
3.2%
valley148606
 
3.1%
central143179
 
3.0%
Other values (101)2811351
59.1%

Most occurring characters

ValueCountFrequency (%)
e3154567
 
9.8%
i2546386
 
7.9%
2348717
 
7.3%
r2179380
 
6.8%
n2139620
 
6.6%
a2062421
 
6.4%
s1940740
 
6.0%
o1927972
 
6.0%
t1711494
 
5.3%
l1505812
 
4.7%
Other values (42)10669072
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24877529
77.3%
Uppercase Letter4787752
 
14.9%
Space Separator2348717
 
7.3%
Other Punctuation122893
 
0.4%
Dash Punctuation49290
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3154567
12.7%
i2546386
10.2%
r2179380
8.8%
n2139620
8.6%
a2062421
8.3%
s1940740
7.8%
o1927972
7.7%
t1711494
 
6.9%
l1505812
 
6.1%
h896851
 
3.6%
Other values (15)4812286
19.3%
Uppercase Letter
ValueCountFrequency (%)
H634727
13.3%
P580074
12.1%
S414337
 
8.7%
M401833
 
8.4%
C307655
 
6.4%
V297061
 
6.2%
I259025
 
5.4%
B258234
 
5.4%
O225263
 
4.7%
R214972
 
4.5%
Other values (13)1194571
25.0%
Other Punctuation
ValueCountFrequency (%)
/115969
94.4%
.6924
 
5.6%
Space Separator
ValueCountFrequency (%)
2348717
100.0%
Dash Punctuation
ValueCountFrequency (%)
-49290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29665281
92.2%
Common2520900
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3154567
 
10.6%
i2546386
 
8.6%
r2179380
 
7.3%
n2139620
 
7.2%
a2062421
 
7.0%
s1940740
 
6.5%
o1927972
 
6.5%
t1711494
 
5.8%
l1505812
 
5.1%
h896851
 
3.0%
Other values (38)9600038
32.4%
Common
ValueCountFrequency (%)
2348717
93.2%
/115969
 
4.6%
-49290
 
2.0%
.6924
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII32186181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3154567
 
9.8%
i2546386
 
7.9%
2348717
 
7.3%
r2179380
 
6.8%
n2139620
 
6.6%
a2062421
 
6.4%
s1940740
 
6.0%
o1927972
 
6.0%
t1711494
 
5.3%
l1505812
 
4.7%
Other values (42)10669072
33.1%

Assessed Value
Real number (ℝ≥0)

SKEWED

Distinct859876
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean897468.7362
Minimum1
Maximum1822089242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 MiB
2022-06-19T21:54:12.713368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile55196
Q1208033
median453528
Q3817236
95-th percentile2005363.8
Maximum1822089242
Range1822089241
Interquartile range (IQR)609203

Descriptive statistics

Standard deviation6680515.448
Coefficient of variation (CV)7.443730548
Kurtosis10313.64644
Mean897468.7362
Median Absolute Deviation (MAD)289177
Skewness75.58785394
Sum2.163658015 × 1012
Variance4.462928665 × 1013
MonotonicityNot monotonic
2022-06-19T21:54:12.820465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12002220
 
0.1%
6000001598
 
0.1%
35001555
 
0.1%
7000001488
 
0.1%
7500001460
 
0.1%
6500001448
 
0.1%
5500001396
 
0.1%
8000001329
 
0.1%
13001304
 
0.1%
5000001186
 
< 0.1%
Other values (859866)2395861
99.4%
ValueCountFrequency (%)
1170
< 0.1%
1041
 
< 0.1%
1343
 
< 0.1%
1361
 
< 0.1%
1382
 
< 0.1%
1401
 
< 0.1%
1421
 
< 0.1%
1441
 
< 0.1%
1461
 
< 0.1%
1481
 
< 0.1%
ValueCountFrequency (%)
18220892421
< 0.1%
16917448811
< 0.1%
13365952941
< 0.1%
11825405791
< 0.1%
10387869171
< 0.1%
10184185471
< 0.1%
10045551751
< 0.1%
9984495571
< 0.1%
9955058711
< 0.1%
9848580151
< 0.1%

Interactions

2022-06-19T21:53:55.195542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:38.992618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:49.197480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:58.866285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:08.246621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:17.500661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:26.777987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:36.215785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:45.848799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:56.238812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:40.198720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:50.249238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:59.899242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:09.267550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:18.531600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:27.793557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:37.222774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:46.891504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:57.293772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:41.410822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:51.292998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:00.907011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:10.285476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:19.549029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:28.823557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:38.245668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:47.953305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:58.344728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:42.612916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:52.359133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:01.942289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:11.293392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:20.590014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:29.956377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:39.368512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:48.998994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:59.400688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:43.672881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:53.511897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:02.974385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:12.335576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:21.598248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:31.015348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:40.457611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:50.005006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:54:00.767932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:44.731844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:54.605105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:03.983344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:13.369493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:22.615712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:32.023533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:41.534903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:51.020763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:54:01.805876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:45.908702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:55.672823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:04.995663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:14.410851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:23.658728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:33.096331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:42.597209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:52.064845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:54:02.850333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:46.952400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:56.742796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:06.025601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:15.426776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:24.699325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:34.115867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:43.677192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:53.086775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:54:03.875965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:48.023480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:52:57.813961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:07.176648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:16.451708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:25.721823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:35.154817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:44.776709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-19T21:53:54.138962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Missing values

2022-06-19T21:54:04.167231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-19T21:54:05.472994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-19T21:54:08.246283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-19T21:54:08.994964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0Year Property BuiltNumber of BathroomsNumber of RoomsNumber of StoriesNumber of UnitsProperty AreaLot AreaAssessor NeighborhoodAssessed Value
01381995.04.012.02.02.02420.02500.0Hunters Point335135
11671942.00.01.01.00.00.0396.0Lower Pacific Heights117011
21752016.02.06.01.00.0957.00.0Hunters Point780810
31761909.01.07.01.01.01500.05500.0Excelsior596720
41791929.03.08.03.00.03045.00.0Russian Hill1301167
51801910.01.05.00.01.01310.00.0Russian Hill1189283
61831922.01.04.01.01.0968.00.0Telegraph Hill403799
71841907.05.016.03.05.02664.01149.0Telegraph Hill117882
81851907.02.08.02.02.01915.01390.0North Beach298191
91861907.05.011.02.02.02738.01441.0Russian Hill1613628

Last rows

Unnamed: 0Year Property BuiltNumber of BathroomsNumber of RoomsNumber of StoriesNumber of UnitsProperty AreaLot AreaAssessor NeighborhoodAssessed Value
241083526661072004.01.03.00.00.0982.00.0Mission Bay856522
241083626661081979.02.07.00.01.02180.00.0Noe Valley970638
241083726661091954.02.06.01.01.01434.03135.0Lake Shore110543
241083826661101925.03.010.01.01.02747.02622.0Noe Valley1325015
241083926661111995.03.07.01.01.02545.03332.0Merced Manor1226932
241084026661121946.02.06.01.01.01535.02495.0Merced Heights321910
241084126661131937.03.09.01.01.01837.02500.0Ingleside Heights411518
241084226661141975.02.07.01.01.01904.00.0Lake Shore945064
241084326661151939.01.07.01.01.01897.04360.0Lakeside1297121
241084426661161978.03.07.01.01.02145.02500.0Ingleside Heights267086